Supplementary appendix

Transcription

1 Supplementary appendix This appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Chen H, Kwong JC, Copes R, et al. Living near major roads and the incidence of dementia, Parkinson s disease, and multiple sclerosis: a populationbased cohort study. Lancet 2016; published online Jan 4. S (16)

2 Supplemental Material Living near major roads and the incidence of dementia, Parkinson s disease, and multiple sclerosis: a population-based cohort study Hong Chen, PhD, 1,2,3 Jeffrey C. Kwong, MD, 1,2,3,4 Ray Copes, MD, 1,3 Karen Tu, MD, 2,4 Paul J. Villeneuve, PhD, 3,5 Aaron van Donkelaar, PhD, 6 Perry Hystad, PhD, 7 Randall V. Martin, full professor, PhD, 6,8 Brian J. Murray, MD, 9 Barry Jessiman, MSc, 10 Andrew S. Wilton, MSc, 2 Alexander Kopp, BA, 2 Richard T. Burnett, PhD 10 1 Public Health Ontario, Toronto, ON, Canada 2 Institute for Clinical Evaluative Sciences, Toronto, ON, Canada 3 Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada 4 Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada 5 Department of Health Sciences, Carleton University, Ottawa, ON, Canada 6 Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada 7 College of Public Health and Human Sciences, Oregon State University, Corvallis, USA 8 Harvard-Smithsonian Centre for Astrophysics, Cambridge, MA, USA 9 Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada 10 Population Studies Division, Health Canada, Ottawa, ON, Canada Correspondence: Hong Chen, PhD Public Health Ontario 480 University Avenue, Suite 300 Toronto, Ontario M5G 1V2 Tel: hong.chen@oahpp.ca 1

3 Table of Contents Ascertainment of Dementia, PD, and MS... 3 Ascertainment of Comorbidities. 3 Area-level Deprivation... 4 North/South Indicator... 4 Ascertainment of Long-term Exposure to PM 2.5 and NO Indirect Adjustment for Unmeasured Covariates... 5 Additional Sensitivity Analyses... 6 Burden Attributable to Traffic Exposure Figure Legends Reference

4 Ascertainment of Dementia, PD, and MS Cases of dementia were defined as any individual having at least one hospital admission with a diagnosis of dementia [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9CM) diagnostic code 46.1, , 294, 331.0, 331.1, 331.5, or ICD-10 code F00-F03, G30 after 2002] or three physician claims (code 290, 331) over a twoyear period or a prescription relating to dementia (e.g., donepezil, galantamine). 1 PD was defined as any person having at least two physician claims (code 332) within a one-year period or a physician claim and a prescription relating to PD (e.g., Monoamine Oxidase B inhibitors, Catechol-O-methyl transferase inhibitors) within six months. 2 Individuals entered the MS database if they had at least one hospital admission with a diagnosis of MS (ICD-9 code 340 or ICD-10 code G35) or five physician claims (code 340) over a two-year period. 3 All three databases have been validated previously using chart review. 1-3 The Ontario Dementia Database has been found to identify individuals ( 20 years of age) with dementia with a sensitivity of 79% and specificity of 99% (positive and negative predictive values of 80% and 99%, respectively). 1 Similarly high sensitivity and specificity were shown for PD and MS databases (PD: sensitivity=78%, specificity 100%, positive predictive value=77%, negative predictive value 100%; MS: sensitivity=84%, specificity=100%, positive predictive value=86%, negative predictive value 100%). 2, 3 Once included in these databases, individuals remain in them until death or termination of Ontario health coverage. People whose diagnosis date was at or before baseline were excluded from the analysis. Ascertainment of Comorbidities We determined prior history of hospital admissions for stroke, coronary heart disease, and arrhythmia using hospital discharge abstracts (ICD codes are listed in the online appendix). To ascertain cohort members with traumatic brain injury, we linked the cohorts to the hospital discharge abstracts and the physician service claims database. Additionally, people with diabetes, hypertension, or heart failure were identified using validated databases of all residents diagnosed 3

5 with these conditions in Ontario. 4-6 The presence of the diagnosis of a specific disease between 1991 and 2001 was defined as the presence of that comorbidity. Area-level Deprivation As a sensitivity analysis, we also derived a variable for neighborhood-level deprivation based on the Ontario Marginalization Index that has been previously developed to quantify the degree of marginalization in health and social well-being across Ontario. 7 It consists of four dimensions thought to underlie the construct of marginalization: residential instability; material deprivation; dependency; and ethnic concentration. North/South Indicator To classify Ontario into northern and southern regions, we created an indicator variable based on the 14 Ontario Local Health Integrated Networks (equivalent to health regions). The Local Health Integrated Networks are responsible for planning, integrating, and funding various local healthcare services across the province of Ontario. Of the 14 Local Health Integrated Networks, two (North East and North West) cover the population living in northern Ontario and were combined to create the indicator (versus the rest of health regions). Ascertainment of Long-term Exposure to PM 2.5 and NO 2 To explore the extent to which exposure to common air pollutants, especially nitrogen dioxide (NO 2 ) and fine particulate matter (particles 2.5μm in diameter or PM 2.5 ), may explain the roadway proximity-outcome association, we obtained for all participants long-term exposures to these two pollutants. Details of exposure assessment of PM 2.5 and NO 2 are reported elsewhere. 8, 9 Briefly, estimates of ground-level concentrations of PM 2.5 were derived from satellite observations of aerosol optical depth in combination with outputs from a global atmospheric chemistry transport model (GEOS-Chem CTM). 9 The PM 2.5 estimates were further adjusted using information on urban land cover, elevation, and aerosol composition using a 4

6 geographically weighted regression. We used estimates between 1998 (the earliest year with available data) and 2001 (the year of cohort inception), thus producing four-year mean concentration of PM 2.5 at a spatial resolution of 1 1km and covering all North America below 70 o N, which includes all of Ontario. These satellite-based estimates of PM 2.5 closely agree with ground measurements at fixed-site monitoring stations across North America (R 2 =0.82, n=1440). 9 Similarly, we derived long-term exposure to NO 2 from a national land-use regression (LUR) model developed from Environment Canada s National Air Pollution Surveillance Network monitoring data, satellite NO 2 estimates, area of industrial land use, road length, and mean summer rain fall. 8 The estimates were further calibrated by incorporating localscale variations of NO 2 from vehicle emissions by applying spatially-varying multipliers that represented distance-decay gradient in NO 2. The final LUR model explained 73% of the variation in annual 2006 measurements of NO 2, with a root mean square error of 2.9 parts per billion (ppb). 8 The resulting LUR NO 2 estimates were available for each year between 1998 and 2001 after applying temporal adjustment. We created estimates of exposure to PM 2.5 and NO 2 for each participant by interpolating the exposure surfaces to their residences in Of the cohorts, the average concentration of PM 2.5 according to subjects residences in 1996 was 9.7 µg/m 3 (range: 1.3 to 19.8 µg/m 3 ), while the average concentration of NO 2 was 15.4 ppb (range: 2.2 to 62.0 ppb). Indirect Adjustment for Unmeasured Covariates To assess the robustness of our findings, we further evaluated the potential influence of unmeasured lifestyle/behavioral variables, especially smoking, obesity, physical activity, and education on our effect estimates. To do this, we used a method to mathematically adjust our HRs for these variables while simultaneously controlling for all variables available in the model (i.e., age, sex, comorbidities, and SES). 11 A description of the method is presented elsewhere. 11 Briefly, this method requires spatial associations between the unmeasured (e.g., smoking, obesity) and observed variables (e.g., age, comorbidities, SES) from an auxiliary dataset. Following previous Canadian studies using this method, 12, 13 we obtained the relationships using data from Ontario respondents to the 1996/1997 cycle of the National Population Health Survey and the 5

7 2000/2001 and 2003 cycles of the Canadian Community Health Survey (Supplemental Table S4). These population-based surveys are conducted routinely by Statistics Canada and cover nearly all household residents aged 12 years or older in Ontario and other provinces. 14, 15 We verified that the distribution of important characteristics was comparable between participants to the MS and dementia/pd cohorts and participants to the three population-based health surveys who had similar age. For example, of survey respondents who were 55 to 85 years old, their mean age was 67.1 years (standard deviation: 8.1), 9.4% had coronary heart disease, and average unemployment among census dissemination areas was 6.6%. These indicate that the survey respondents constituted a representative sample of the MS and dementia/pd cohorts. This information on the relationships between the unmeasured (e.g., smoking, obesity) and observed variables (e.g., age, comorbidities, SES) along with estimated associations between these unmeasured variables and the incidence of dementia, PD, and MS from the literature were used to estimate their impact on our HRs. Based on the strength of evidence from recent systematic reviews of dementia, PD, and MS (Supplemental Table S5), we adjusted for all four variables for our HRs with dementia, smoking and physical activity with PD, and smoking with MS. Additional Sensitivity Analyses We conducted additional data linkage for all cohort members, and we ascertained the status of their comorbidities according to the date of diagnosis during follow-up. In addition, we created new neighborhood-level SES variables by further incorporating 2006 Canadian Census dissemination area data. Because publicly available dissemination-area data from 2011 Canadian Census are currently limited, we were unable to incorporate data from this census. We conducted a new sensitivity analysis by modeling comorbidities and neighborhood-level SES as timevarying variables. As shown in Supplemental Table S6, the risk estimates were not altered materially. To further investigate the extent to which our effect estimates were sensitive to adjustment for other geographically-variable sociodemographic and other related healthcare indicators, we 6

8 created two new sets of covariates: rurality and neighborhood-level percentage of visible minority. We conducted sensitivity analyses by additionally controlling for these two new variables. To represent rurality, we created the variable based on the Rurality Index for Ontario which takes into account community population density, travel time to nearest basic referral centre, and travel time to nearest advanced referral centre. 25 The Rurality Index has a value ranging from 0 to 100, with 0-39 considered as urban and 40 and above considered rural. In addition, we derived a variable for % of visible minority using the 2001 Canadian Census dissemination data. We observed that the associations between traffic proximity and incidence of dementia, PD, and MS were virtually unchanged after additional adjustment for these two covariates (data not shown). Burden Attributable to Traffic Exposure To quantify the burden of dementia incidence attributed to long-term exposure to heave traffic, we derived attributable fraction using the formula as follows: AF = (RR-1)/RR. 26 Where AF is the attributable fraction (i.e., burden attributable to risk factor such as traffic exposure) and RR is the adjusted hazard ratio. 7

9 List of Supplemental Figures Figure S1. Distribution of residential distance to nearest major roadways among (A) MS Cohort and (B) Dementia/PD Cohort in Ontario, Canada in Figure S2. Directed acyclic graph showing variables of interest and their causal association 8

10 Supplemental Table S1. Distribution of residential distance to the nearest major roadway among the study population in 1996 Exposure MS Cohort * (N=4,372,720) Median (IQR) or n (%) Dementia/PD Cohort * (N=2,165,268) Distance to major roads or highways (m) 207 (320) 199 (310) Distance by categories (m) <50 754,008 (17.2%) 402,600 (18.6%) ,895 (11.1%) 234,535 (10.8%) ,706 (20.6%) 447,853 (20.7%) ,542 (14.9%) 325,861 (15.1%) ,355,249 (31.0%) 645,635 (29.8%) > ,320 (5.2%) 108,784 (5.0%) * MS Cohort comprised all adults aged years and Dementia/PD Cohort comprised all adults aged years in Ontario, Canada. 9

11 Supplemental Table S2. Hazard ratios (HRs) and 95% confidence intervals (95% CI) for the associations between exposure to NO 2 and PM 2.5 and the risk of incident PD, PD, and MS in Ontario, during the follow-up period * Air pollutants HR 95% CI HR 95% CI HR 95% CI Dementia Parkinson's Disease Multiple Sclerosis NO PM * Cox proportional hazards model with age as time axis, stratified by an indicator for living in the Greater Toronto Area or not, adjusted for sex, residential proximity to major roadways (categorical variable), history of diabetes, hypertension, coronary heart disease, stroke, congestive heart failure, arrhythmia, and traumatic brain injury, income quintile, urban/rural indicator, census divisionlevel unemployment, education, and recent immigrants, as well as the subtraction of these variables at the census dissemination level from their census-division. Long-term exposure to NO 2 and PM 2.5 was assessed using land use regression model and satellite-based observations, respectively, and was derived for all study participants, according to their baseline residence. The HRs were expressed for every interquartile-range increase in exposure (NO 2 : 11.3ppb; PM 2.5 : 3.4 μg/m 3 ). 10

12 Supplemental Table S3. Hazard ratios (HRs) and 95% confidence intervals (95% CI) for the associations between residential proximity to major roadways in 1996 and the risk of incident dementia and PD in Ontario, during the follow-up period , restricting to people aged 65 years Dementia Parkinson s Disease Residential distance Restricted to people aged Restricted to people aged to major roads or Main model 65 years Main model 65 years highways * HR 95% CI HR 95% CI HR 95% CI HR 95% CI Distance by category <50 m m m m >300 m Reference Reference Reference Reference Log(distance) * Major traffic roads include primary urban roads and arterial roads whereas highways include expressways and primary and secondary highways, as defined by Ontario Government Road Network Data Standards. Cox proportional hazards model with age as time axis, stratified by an indicator for living in the Greater Toronto Area or not, adjusted for sex, history of diabetes, hypertension, coronary heart disease, stroke, congestive heart failure, arrhythmia, and traumatic brain injury, income quintile, urban/rural indicator, census division-level unemployment, education, and recent immigrants, as well as the subtraction of these variables at the census dissemination level from their census-division. Distance was fitted as a continuous variable, using natural logarithm of distance. The HRs were expressed per interquartile-range increase in distance (Dementia/PD Cohort: 310 m). 11

13 Supplemental Table S4. Estimates of the multivariate relationship (referred to as delta) between the observed covariates in the survival analysis * and adjustment factors, by outcome and categories of distance Adjustment Factors Smoking Obesity Physical activity Education Outcomes Delta Delta Delta Delta Dementia <50 m m m m Parkinson's disease <50 m m m m Multiple sclerosis <50 m m m m * Observed covariates included age, sex, history of diabetes, hypertension, coronary heart disease, stroke, congestive heart failure, arrhythmia, and traumatic brain injury, income quintile, an indicator for living in the Greater Toronto Area or not, urban/rural indicator, neighborhood-level unemployment, education, and recent immigrants. For MS analysis, latitude was also included. Data of smoking, obesity, physical activity, and educational attainment were obtained from Ontario respondents to the 1996 cycle of National Population Health Survey and the 2000/2001 and 2003 cycles of Canadian Community Health Survey. For dementia and PD, respondents aged years at the time of the surveys were included (n=30,818) and for MS, those who were 20 to 55 years old were included (n=31,635) 12

14 Supplemental Table S5. Hazard ratios (HR) and 95% confidence intervals for the associations between adjustment factors and the risk of incident PD, PD, and MS, according to the latest systematic reviews of these three diseases Adjustment Factors Smoking * Obesity Physical activity Education Outcome HR 95% CI HR 95% CI HR 95% CI HR 95% CI References Dementia , 21, 22, 23 PD , 24 MS * Current smokers versus never smokers Obese versus normal weight High level versus low level physical activity Low versus high education 13

15 Supplemental Table S6. Hazard ratios (HR) and 95% confidence intervals (95% CI) for the associations between residential proximity to major roadways in 1996 and the risks of incident dementia, Parkinson s disease (PD), and multiple sclerosis (MS) in Ontario, during the follow-up period , by modeling comorbidities and SES as time-fixed and time-dependent variables, respectively * Residential distance to major roads or highways, by neurological disease Time-fixed comorbidities/ses at baseline Time-varying comorbidities/ses during follow-up HR 95% CI HR 95% CI Incidence of Dementia <50 m m m m >300 m Reference Reference Incidence of PD <50 m m m m >300 m Reference Reference Incidence of MS <50 m m m m >300 m Reference Reference * Comorbidities included diabetes, hypertension, coronary heart disease, stroke, congestive heart failure, arrhythmia, and traumatic brain injury while SES included neighborhood-level income quintile, unemployment rate, education, and recent immigrants 14

16 (A) MS Cohort (N=4,372,720) (B) Dementia/PD Cohort (N=2,165,268) Supplemental Figure S1. Distribution of residential distance to nearest major roadways among (A) MS Cohort and (B) Dementia/PD Cohort in Ontario, Canada in

17 Individual SES (e.g., income, education, employment) Age, gender Region, urban residency, neighborhood SES Behavioral factors (e.g., smoking, BMI) Comorbidities (e.g., stroke, diabetes) Living close to major roadways - Air pollution - Noise - Possibly other factors (e.g., lack of physical activity) Neurodegenerative diseases (e.g., dementia) Supplemental, Figure S2. Directed acyclic graph showing variables of interest and their causal association 16

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